#Ruchi Moondra
#Assignment: World Happiness Analysis
#Loading the data
worldh <- read.csv("C:/Users/Ruchi/Desktop/Ruchi/Rutgers/Multivariate/Dataset/WH_2017.csv")

#Loading packages required for the analysis
library(plyr)
library(plotly)
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library(dplyr)
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library(tidyverse)
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library(lubridate)
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library(caTools)
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library(reshape2)
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library(tidyr)
library(corrgram)       
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library(corrplot)
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library(cowplot)
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library(ggpubr)
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library(plot3D)
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library(car)
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library(FactoMineR)
library(factoextra)
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library(corrplot)
library(mice)
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#View the data
View(worldh)
#Displays the first few rows of the dataset
head(worldh)
##       Country Happiness.Rank Happiness.Score Whisker.high Whisker.low
## 1      Norway              1           7.537     7.594445    7.479556
## 2     Denmark              2           7.522     7.581728    7.462272
## 3     Iceland              3           7.504     7.622030    7.385970
## 4 Switzerland              4           7.494     7.561772    7.426227
## 5     Finland              5           7.469     7.527542    7.410458
## 6 Netherlands              6           7.377     7.427426    7.326574
##   Economy..GDP.per.Capita.   Family Health..Life.Expectancy.   Freedom
## 1                 1.616463 1.533524                0.7966665 0.6354226
## 2                 1.482383 1.551122                0.7925655 0.6260067
## 3                 1.480633 1.610574                0.8335521 0.6271626
## 4                 1.564980 1.516912                0.8581313 0.6200706
## 5                 1.443572 1.540247                0.8091577 0.6179509
## 6                 1.503945 1.428939                0.8106961 0.5853845
##   Generosity Trust..Government.Corruption. Dystopia.Residual
## 1  0.3620122                     0.3159638          2.277027
## 2  0.3552805                     0.4007701          2.313707
## 3  0.4755402                     0.1535266          2.322715
## 4  0.2905493                     0.3670073          2.276716
## 5  0.2454828                     0.3826115          2.430182
## 6  0.4704898                     0.2826618          2.294804
#Display the structure of the attributes
str(worldh)
## 'data.frame':    155 obs. of  12 variables:
##  $ Country                      : Factor w/ 155 levels "Afghanistan",..: 105 38 58 133 45 99 26 100 132 7 ...
##  $ Happiness.Rank               : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Happiness.Score              : num  7.54 7.52 7.5 7.49 7.47 ...
##  $ Whisker.high                 : num  7.59 7.58 7.62 7.56 7.53 ...
##  $ Whisker.low                  : num  7.48 7.46 7.39 7.43 7.41 ...
##  $ Economy..GDP.per.Capita.     : num  1.62 1.48 1.48 1.56 1.44 ...
##  $ Family                       : num  1.53 1.55 1.61 1.52 1.54 ...
##  $ Health..Life.Expectancy.     : num  0.797 0.793 0.834 0.858 0.809 ...
##  $ Freedom                      : num  0.635 0.626 0.627 0.62 0.618 ...
##  $ Generosity                   : num  0.362 0.355 0.476 0.291 0.245 ...
##  $ Trust..Government.Corruption.: num  0.316 0.401 0.154 0.367 0.383 ...
##  $ Dystopia.Residual            : num  2.28 2.31 2.32 2.28 2.43 ...
# Adding another column name "Continent"
worldh$Continent <- NA

# Deleting unnecessary columns (Whisker.high and Whisker.low)
worldh <- worldh[, -c(4,5)]

# Changing the name of columns
colnames (worldh) <- c("Country", "Happiness.Rank", "Happiness.Score",
                       "Economy", "Family", "Life.Expectancy", "Freedom", "Generosity",
                       "Trust", "Dystopia.Residual", "Continent")

# Adding the values for Continent name in the data.

worldh$Continent[which(worldh$Country %in% c("Israel", "United Arab Emirates", "Singapore", "Thailand", "Taiwan Province of China",
                                             "Qatar", "Saudi Arabia", "Kuwait", "Bahrain", "Malaysia", "Uzbekistan", "Japan",
                                             "South Korea", "Turkmenistan", "Kazakhstan", "Turkey", "Hong Kong S.A.R., China", "Philippines",
                                             "Jordan", "China", "Pakistan", "Indonesia", "Azerbaijan", "Lebanon", "Vietnam",
                                             "Tajikistan", "Bhutan", "Kyrgyzstan", "Nepal", "Mongolia", "Palestinian Territories",
                                             "Iran", "Bangladesh", "Myanmar", "Iraq", "Sri Lanka", "Armenia", "India", "Georgia",
                                             "Cambodia", "Afghanistan", "Yemen", "Syria"))] <- "Asia"
worldh$Continent[which(worldh$Country %in% c("Norway", "Denmark", "Iceland", "Switzerland", "Finland",
                                             "Netherlands", "Sweden", "Austria", "Ireland", "Germany",
                                             "Belgium", "Luxembourg", "United Kingdom", "Czech Republic",
                                             "Malta", "France", "Spain", "Slovakia", "Poland", "Italy",
                                             "Russia", "Lithuania", "Latvia", "Moldova", "Romania",
                                             "Slovenia", "North Cyprus", "Cyprus", "Estonia", "Belarus",
                                             "Serbia", "Hungary", "Croatia", "Kosovo", "Montenegro",
                                             "Greece", "Portugal", "Bosnia and Herzegovina", "Macedonia",
                                             "Bulgaria", "Albania", "Ukraine"))] <- "Europe"
worldh$Continent[which(worldh$Country %in% c("Canada", "Costa Rica", "United States", "Mexico",  
                                             "Panama","Trinidad and Tobago", "El Salvador", "Belize", "Guatemala",
                                             "Jamaica", "Nicaragua", "Dominican Republic", "Honduras",
                                             "Haiti"))] <- "North America"
worldh$Continent[which(worldh$Country %in% c("Chile", "Brazil", "Argentina", "Uruguay",
                                             "Colombia", "Ecuador", "Bolivia", "Peru",
                                             "Paraguay", "Venezuela"))] <- "South America"
worldh$Continent[which(worldh$Country %in% c("New Zealand", "Australia"))] <- "Australia"
worldh$Continent[which(is.na(worldh$Continent))] <- "Africa"

# Moving the Continent column at the second position.

worldh <- worldh %>% select(Country,Continent, everything())

str(worldh)
## 'data.frame':    155 obs. of  11 variables:
##  $ Country          : Factor w/ 155 levels "Afghanistan",..: 105 38 58 133 45 99 26 100 132 7 ...
##  $ Continent        : chr  "Europe" "Europe" "Europe" "Europe" ...
##  $ Happiness.Rank   : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Happiness.Score  : num  7.54 7.52 7.5 7.49 7.47 ...
##  $ Economy          : num  1.62 1.48 1.48 1.56 1.44 ...
##  $ Family           : num  1.53 1.55 1.61 1.52 1.54 ...
##  $ Life.Expectancy  : num  0.797 0.793 0.834 0.858 0.809 ...
##  $ Freedom          : num  0.635 0.626 0.627 0.62 0.618 ...
##  $ Generosity       : num  0.362 0.355 0.476 0.291 0.245 ...
##  $ Trust            : num  0.316 0.401 0.154 0.367 0.383 ...
##  $ Dystopia.Residual: num  2.28 2.31 2.32 2.28 2.43 ...
#Converting the Continent values into factorial.
worldh$Continent <- as.factor(worldh$Continent)

# Finding the correlation between numerical columns
Num.cols <- sapply(worldh, is.numeric)
Cor.data <- cor(worldh[, Num.cols])
corrplot(Cor.data, method = 'color')

#Analysis: We can see there is an inverse correlation between "Happiness Rank" and all the other numerical variables. In other words, the lower the happiness rank, the higher the happiness score, and the higher the other seven factors that contribute to happiness. So let's remove the happiness rank, and see the correlation again.

# Create a correlation plot
newdatacor = cor(worldh[c(3:10)])
corrplot(newdatacor, method = "number")

#Analysis: In the above cor plot, Economy, life expectancy, and family play the most significant role in contributing to happiness. 
#Trust and generosity have the lowest impact on the happiness score.

#Plotting ScatterPLot
plot_ly(data = worldh, 
        x=~Economy, y=~Happiness.Score, type = "scatter",
        text = ~paste("Country:", Country)) %>% 
  layout(title = "Happiness and GDP", 
         xaxis = list(title = "GDP per Capita"),
         yaxis = list(title = "Happiness Score"))
## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
#Analysis: This interactive scatterplot shows that there is a strong positive correlation between GDP and Happiness.

#Let's do multiple Regression
dat <- worldh[c("Happiness.Score","Economy","Generosity")]
head(dat)
##   Happiness.Score  Economy Generosity
## 1           7.537 1.616463  0.3620122
## 2           7.522 1.482383  0.3552805
## 3           7.504 1.480633  0.4755402
## 4           7.494 1.564980  0.2905493
## 5           7.469 1.443572  0.2454828
## 6           7.377 1.503945  0.4704898
plot(dat)

#It seems like there is a positive correlation between economy and happiness score but this is not true between happiness score
#and generosity.

#3D plot of same
scatter3D(dat$Generosity, dat$Economy, dat$Happiness.Score, phi = 0, bty = "g",
          pch = 20, cex = 2, ticktype = "detailed",
          main = "Happiness data", xlab = "Generosity",
          ylab ="Economy", zlab = "Happiness.Score")

#From the scatter plot we cannot determine that combination of high economy and generosity leads to greater happiness score. 
#This is something we have to conclude after analyzing the effect of these 2 taken together.


# Checking the outliers in the dataset using the boxplot.
names(worldh)[4] <- "Happiness_Score"

ggplot(worldh, aes(x=Continent, y= Happiness_Score, colour = Continent)) + 
  
  geom_boxplot() + 
  
  theme(axis.text.x = element_text(angle = 60, hjust = 1)) + 
  
  labs(title = "Happiness Score Boxplot",
       
       x = "Continent",
       
       y = "Happiness Score")

##Checking for normality using shaprio test

qqPlot(worldh$Economy)

## [1] 155  93
shapiro.test(worldh$Economy)
## 
##  Shapiro-Wilk normality test
## 
## data:  worldh$Economy
## W = 0.96977, p-value = 0.00175
#p-value is greater than 0.05 implying that the data is not significantly different from normal distribution 
qqPlot(worldh$Family)

## [1] 155 152
shapiro.test(worldh$Family)
## 
##  Shapiro-Wilk normality test
## 
## data:  worldh$Family
## W = 0.91152, p-value = 4.186e-08
qqPlot(worldh$Life.Expectancy)

## [1] 139 106
shapiro.test(worldh$Life.Expectancy)
## 
##  Shapiro-Wilk normality test
## 
## data:  worldh$Life.Expectancy
## W = 0.94602, p-value = 1.135e-05
qqPlot(worldh$Freedom)

## [1] 140 130
shapiro.test(worldh$Freedom)
## 
##  Shapiro-Wilk normality test
## 
## data:  worldh$Freedom
## W = 0.95945, p-value = 0.0001673
qqPlot(worldh$Generosity)

## [1] 114  81
shapiro.test(worldh$Generosity)
## 
##  Shapiro-Wilk normality test
## 
## data:  worldh$Generosity
## W = 0.95783, p-value = 0.0001184
qqPlot(worldh$Trust)

## [1]  26 151
shapiro.test(worldh$Trust)
## 
##  Shapiro-Wilk normality test
## 
## data:  worldh$Trust
## W = 0.83902, p-value = 9.204e-12
#Family,Life expectancy and trust variables are not normally distributed

####PCA################
act_col <- c(3, 5:10)

act_col
## [1]  3  5  6  7  8  9 10
happiness_new <- worldh[, act_col]

cor(happiness_new)
##                 Happiness.Rank     Economy      Family Life.Expectancy
## Happiness.Rank       1.0000000 -0.81324364 -0.73675268     -0.78071584
## Economy             -0.8132436  1.00000000  0.68829631      0.84307664
## Family              -0.7367527  0.68829631  1.00000000      0.61208006
## Life.Expectancy     -0.7807158  0.84307664  0.61208006      1.00000000
## Freedom             -0.5516078  0.36987339  0.42496576      0.34982679
## Generosity          -0.1326198 -0.01901125  0.05169263      0.06319149
## Trust               -0.4058423  0.35094410  0.23184139      0.27975198
##                    Freedom  Generosity      Trust
## Happiness.Rank  -0.5516078 -0.13261979 -0.4058423
## Economy          0.3698734 -0.01901125  0.3509441
## Family           0.4249658  0.05169263  0.2318414
## Life.Expectancy  0.3498268  0.06319149  0.2797520
## Freedom          1.0000000  0.31608271  0.4991828
## Generosity       0.3160827  1.00000000  0.2941595
## Trust            0.4991828  0.29415945  1.0000000
happiness_pca <- prcomp(happiness_new,scale=TRUE)

summary(happiness_pca)
## Importance of components:
##                           PC1    PC2     PC3     PC4     PC5     PC6
## Standard deviation     1.9426 1.1630 0.82011 0.73722 0.60113 0.40173
## Proportion of Variance 0.5391 0.1932 0.09608 0.07764 0.05162 0.02305
## Cumulative Proportion  0.5391 0.7324 0.82844 0.90608 0.95770 0.98076
##                            PC7
## Standard deviation     0.36702
## Proportion of Variance 0.01924
## Cumulative Proportion  1.00000
(eigen_happiness <- happiness_pca$sdev^2)
## [1] 3.7738458 1.3526400 0.6725747 0.5434929 0.3613563 0.1613830 0.1347073
eigen_happiness
## [1] 3.7738458 1.3526400 0.6725747 0.5434929 0.3613563 0.1613830 0.1347073
names(eigen_happiness) <- paste("PC",1:7,sep="")

sumlambdas <- sum(eigen_happiness)
sumlambdas
## [1] 7
propvar <- eigen_happiness/sumlambdas
propvar
##        PC1        PC2        PC3        PC4        PC5        PC6 
## 0.53912084 0.19323429 0.09608209 0.07764184 0.05162233 0.02305471 
##        PC7 
## 0.01924390
cumvar_happiness <- cumsum(propvar)
cumvar_happiness
##       PC1       PC2       PC3       PC4       PC5       PC6       PC7 
## 0.5391208 0.7323551 0.8284372 0.9060791 0.9577014 0.9807561 1.0000000
matlambdas <- rbind(eigen_happiness,propvar,cumvar_happiness)
rownames(matlambdas) <- c("Eigenvalues","Prop. variance","Cum. prop. variance")

round(matlambdas,4)
##                        PC1    PC2    PC3    PC4    PC5    PC6    PC7
## Eigenvalues         3.7738 1.3526 0.6726 0.5435 0.3614 0.1614 0.1347
## Prop. variance      0.5391 0.1932 0.0961 0.0776 0.0516 0.0231 0.0192
## Cum. prop. variance 0.5391 0.7324 0.8284 0.9061 0.9577 0.9808 1.0000
summary(happiness_pca)
## Importance of components:
##                           PC1    PC2     PC3     PC4     PC5     PC6
## Standard deviation     1.9426 1.1630 0.82011 0.73722 0.60113 0.40173
## Proportion of Variance 0.5391 0.1932 0.09608 0.07764 0.05162 0.02305
## Cumulative Proportion  0.5391 0.7324 0.82844 0.90608 0.95770 0.98076
##                            PC7
## Standard deviation     0.36702
## Proportion of Variance 0.01924
## Cumulative Proportion  1.00000
happiness_pca$rotation
##                        PC1         PC2          PC3        PC4         PC5
## Happiness.Rank   0.4788810 -0.08240033  0.055747166 -0.0423546  0.05297915
## Economy         -0.4551507  0.25969334  0.004308571 -0.2549601 -0.10093319
## Family          -0.4133402  0.18852086 -0.186020546  0.3805632  0.72959861
## Life.Expectancy -0.4371899  0.23420596 -0.171512604 -0.3328363 -0.40772765
## Freedom         -0.3364051 -0.40606620  0.218916193  0.6702964 -0.43207360
## Generosity      -0.1052528 -0.67002041 -0.695293769 -0.2063681  0.05870829
## Trust           -0.2779915 -0.47070149  0.633636921 -0.4309386  0.31355300
##                         PC6         PC7
## Happiness.Rank  0.853440489  0.16677560
## Economy         0.117920263  0.79767244
## Family          0.259451316 -0.12061794
## Life.Expectancy 0.393633806 -0.54094969
## Freedom         0.181306473  0.07183781
## Generosity      0.005882616  0.10243048
## Trust           0.050645253 -0.11435316
print(happiness_pca)
## Standard deviations (1, .., p=7):
## [1] 1.9426389 1.1630305 0.8201065 0.7372197 0.6011292 0.4017250 0.3670249
## 
## Rotation (n x k) = (7 x 7):
##                        PC1         PC2          PC3        PC4         PC5
## Happiness.Rank   0.4788810 -0.08240033  0.055747166 -0.0423546  0.05297915
## Economy         -0.4551507  0.25969334  0.004308571 -0.2549601 -0.10093319
## Family          -0.4133402  0.18852086 -0.186020546  0.3805632  0.72959861
## Life.Expectancy -0.4371899  0.23420596 -0.171512604 -0.3328363 -0.40772765
## Freedom         -0.3364051 -0.40606620  0.218916193  0.6702964 -0.43207360
## Generosity      -0.1052528 -0.67002041 -0.695293769 -0.2063681  0.05870829
## Trust           -0.2779915 -0.47070149  0.633636921 -0.4309386  0.31355300
##                         PC6         PC7
## Happiness.Rank  0.853440489  0.16677560
## Economy         0.117920263  0.79767244
## Family          0.259451316 -0.12061794
## Life.Expectancy 0.393633806 -0.54094969
## Freedom         0.181306473  0.07183781
## Generosity      0.005882616  0.10243048
## Trust           0.050645253 -0.11435316
happiness_pca$x
##                PC1         PC2          PC3           PC4          PC5
##   [1,] -3.57859076 -1.07900554  0.449013005 -0.1789550946  0.203059763
##   [2,] -3.64618244 -1.48980003  0.990024008 -0.4608502434  0.573909482
##   [3,] -3.21516819 -0.86940405 -1.236637827  0.4295796131 -0.057504412
##   [4,] -3.62968569 -0.90598652  1.082899829 -0.4344483627  0.241576488
##   [5,] -3.43368285 -0.85829325  1.429869160 -0.2687758954  0.449975887
##   [6,] -3.16035632 -1.46201066 -0.328555808 -0.5222806932  0.034869374
##   [7,] -3.28560398 -1.33882788 -0.131583784 -0.3238333967  0.059282840
##   [8,] -3.57677808 -2.13014748  0.105154667 -0.6567639162  0.592413042
##   [9,] -3.49956834 -1.54378394  0.741862247 -0.6603497428  0.328410764
##  [10,] -3.36712682 -1.56090798 -0.298389383 -0.4704326429  0.206716019
##  [11,] -1.89146538  0.54001301 -1.077606751 -0.3075777203 -0.263070983
##  [12,] -1.84668885  0.35636402 -0.105505461  0.9083457772 -0.466672573
##  [13,] -2.79237423 -0.36176314  0.041401371 -0.0658852016 -0.088381006
##  [14,] -2.39916192 -0.20881363 -0.916697961 -0.1296829567 -0.184109722
##  [15,] -3.26523471 -1.20263917 -0.100086896 -0.4336887413  0.433114686
##  [16,] -2.90506434 -0.71664210  0.286038649 -0.3193487783  0.170536280
##  [17,] -2.68665200 -0.02123863  0.628818619 -0.1796474566  0.060226074
##  [18,] -3.37226839 -0.55115221  0.853494312 -0.5044411931  0.003137049
##  [19,] -2.83869138 -1.30732108 -0.687932287 -0.7058434470  0.424782929
##  [20,] -1.41918809  0.47802549 -1.037476067 -0.4497009465 -0.351629230
##  [21,] -2.82447224 -1.31479390  0.716614012 -0.6150934463 -0.230138049
##  [22,] -1.16451407  0.80030259  0.130516142  0.5166461105  0.251516346
##  [23,] -1.53624301  1.65570184  0.031308277  0.8442186950 -0.452093769
##  [24,] -1.33181556  1.27459580  0.107091847  0.9230011653 -0.222552066
##  [25,] -1.00871753  0.94285709  0.522286962 -0.0762243420 -0.356430525
##  [26,] -3.61831619 -1.42046514  1.369127960 -1.6878178770  0.192185459
##  [27,] -2.60346850 -1.47688651 -1.691335457  0.0231127723 -0.134154767
##  [28,] -1.89299703  0.18851224  0.635982360  0.6056855296 -0.189612638
##  [29,] -0.65417688 -0.24797305 -0.393243481  0.9044988704 -0.320881822
##  [30,] -1.47794544  0.55531392 -0.219829932  0.8289947492 -0.498043555
##  [31,] -1.99212365  0.96902058  0.607000570 -0.1295288111 -0.237185195
##  [32,] -1.55148595 -1.23419687 -2.276442976  0.7917838221 -0.286220885
##  [33,] -1.43534318  1.07334010 -0.851359066 -0.2886707820 -0.122700987
##  [34,] -1.83105589  1.41004449 -0.549688279  0.0881812476 -0.042525816
##  [35,] -3.20075133 -1.56968139  1.610097003 -1.2013643237  0.129486212
##  [36,] -0.79703459  0.96776524 -0.092367101  0.9897397046 -0.079930096
##  [37,] -1.66551614  0.20129398  1.372031638 -0.5191461299  0.304637918
##  [38,] -1.04702380  0.24183492 -1.102742908  0.9617409025 -0.232021779
##  [39,] -1.80155406  0.02716588  0.651292246 -0.3460029121 -0.137015194
##  [40,] -0.91643705  1.95923399 -0.577353743  0.1050966205  0.371143609
##  [41,] -1.91561522 -0.03013221  1.202980084 -0.1176241826  0.013456985
##  [42,] -0.93835238 -0.17505995 -1.395959938 -0.1647706719 -0.073508572
##  [43,] -0.58530567 -0.33576072 -0.362186095  0.2275904509  0.028287419
##  [44,] -0.84280002  0.54550115  0.470794033  0.2460946386 -0.151271947
##  [45,] -0.17441884  0.98166526  0.628192537  0.5092685524 -0.358848890
##  [46,] -1.32287800  0.99613248 -0.088869818  0.8807850976 -0.270599346
##  [47,] -1.56548051 -1.96894618  0.025623351  1.0336480551  0.752459323
##  [48,] -1.02931306  1.99551163 -0.849986193 -0.4690609301  0.110611821
##  [49,] -0.54975375  1.89632080  0.179311508  0.7481764976  0.351493322
##  [50,] -0.10770319 -0.38495000  0.179694864  0.8154683830 -0.598104104
##  [51,] -2.00906491  1.01402221  0.594913430  0.0368696061 -0.338516257
##  [52,] -0.30626081  2.67920504  0.003607533  0.1467905922  0.536153498
##  [53,]  0.02588527  1.40016408  0.752302860 -0.8006129099  0.221427307
##  [54,] -0.63166815  1.36162187 -0.174296089  0.0001178258  0.350049070
##  [55,] -0.71913038  1.48977420 -0.623146362 -1.1600014202 -0.648640204
##  [56,]  0.59692850  1.12916734 -0.851470784 -0.0079089301  0.248542490
##  [57,] -0.36189391  1.27182198 -0.180236277  0.5317302292 -0.963852118
##  [58,] -0.11082872 -0.11770014 -0.056839149  1.2389985644 -0.389824396
##  [59,] -0.98489847 -0.45874536  0.709833059 -0.0473985221  1.304616725
##  [60,] -0.91720786  0.56386753  0.064209941  0.2306271200  0.195240315
##  [61,] -1.33555987  0.11545851 -0.031662720 -0.4928707486 -0.672457281
##  [62,] -1.52801505  0.59772943 -0.585409784  0.8811346283 -0.560917982
##  [63,] -0.19366186  0.99368154  0.110905915  0.6029463081 -0.495166180
##  [64,] -0.61297600 -0.16425091 -1.063854732  0.3318621961 -0.599684897
##  [65,] -0.67319291  0.90830926 -0.901750144 -0.6308319025 -0.842393336
##  [66,] -1.37755476  0.82729656  0.939028050  0.2730222663  0.307097691
##  [67,] -0.59575435  0.98895764  0.274504251 -0.3827137907  0.836595860
##  [68,] -0.38878436  0.62892988  0.284154207  0.7346851340  0.142668841
##  [69,] -0.23750484  1.72748114  0.561092208 -0.1217171325  0.319565230
##  [70,] -0.57824668  0.30521353 -0.224895203  0.8816864072  0.468521131
##  [71,] -2.26766775 -0.84938034  0.192306160 -1.3411506615 -0.284593363
##  [72,] -0.15545867 -0.20919380  0.393974359  1.2567199316 -0.272985584
##  [73,]  0.26648527  1.26041636 -0.788563334 -0.5989321997  0.289843681
##  [74,] -0.17409237  0.45745866  0.302882708  0.1632081830 -0.040863027
##  [75,]  0.05903833  2.29598688 -0.209045390 -0.5832185749  0.412109038
##  [76,] -0.33197617  0.37519103 -0.443981039  0.7498006646 -0.090676301
##  [77,]  0.33452834  0.93092983 -0.692061430 -0.9926451631 -0.682748842
##  [78,]  0.58314487  0.27905443 -0.962872710 -0.5307628055  0.152427759
##  [79,] -0.10269560  1.60241061  0.475974808  0.6817841380 -1.008908184
##  [80,]  1.65067783 -0.52569174 -0.157235263 -1.2057548562 -0.456481633
##  [81,] -0.06156536 -1.40892526 -2.512404729  0.2394920256  0.046607632
##  [82,]  0.29079981  2.17075839  0.002738907 -0.4736455003  0.946715989
##  [83,]  0.23374620  1.20623716 -0.385653862 -0.9171312566  0.385868835
##  [84,]  0.95528913  0.92988070  1.127073631 -0.0789082453 -1.299423528
##  [85,] -0.01294978  0.82009722  1.426935253 -0.1265528767  0.014506452
##  [86,] -0.61830356  0.15813968  0.317390932  0.9581800194  0.019936013
##  [87,]  0.32950064  2.90486958  0.113447802 -1.1721905755  0.168634474
##  [88,]  0.24559822  0.81460027 -0.880930177 -0.6020821659 -0.386340879
##  [89,] -0.73663365  1.55054859 -0.029805130  0.7699185416 -0.689563348
##  [90,]  0.74987016  0.76586740 -1.507612532 -0.9012619240 -0.308836785
##  [91,]  0.70128428  0.26860970 -0.323608229 -0.0055837434 -0.074890168
##  [92,]  0.21536946  0.60967600 -0.609945679 -0.2823611999 -0.067468059
##  [93,]  1.77607777 -2.84369936  1.672246024  0.6833648896 -0.233484007
##  [94,] -0.18812117 -0.20843391 -0.035343368  0.9733861324 -0.464525643
##  [95,]  1.58055347 -0.06025335 -0.183208747  1.2092278521  0.720584836
##  [96,]  0.40550577 -0.57273804  0.205847645  0.5808786866  0.271113368
##  [97,] -0.32807657 -1.66482410 -0.758504942  0.1740488023  0.512518641
##  [98,]  0.31763161 -0.78037975 -1.505172879  0.7683671383  0.322768775
##  [99,]  0.82251806 -0.98807412 -0.964680717  0.4676614686  0.021333727
## [100,] -0.05273460  0.19389174 -1.229961339  0.5241006045  0.580890276
## [101,]  0.62388282  0.34776390  0.508012972  1.4008505745  0.728460741
## [102,]  1.09942118  1.29185070  0.809408296 -0.6238623373 -0.752689428
## [103,]  1.12023428  1.01426108  0.288308146 -0.2669674840  0.266459312
## [104,]  1.00521750  0.77019321  0.549520524 -0.5853882128 -0.116687457
## [105,]  0.13334311  1.88281735 -0.420362235  0.1179282388  0.283636533
## [106,]  2.57469420 -0.84999467 -0.137953464  0.5892498983  0.718610429
## [107,]  2.04145380 -0.55883103  0.130249902  0.9102414959 -0.050471795
## [108,]  1.10977613 -0.09693228 -1.135804784 -1.5008363607 -1.078465629
## [109,]  0.86542303  0.56091616 -0.165689579 -0.4982266160 -1.451834146
## [110,]  1.35883007 -0.44017599  0.832847725  0.0578689887 -1.219182239
## [111,]  0.86081961  0.33814896  1.104860730  1.0453241874 -0.307614005
## [112,]  1.34738358 -1.47451269 -1.030803394  0.5448680342  0.028979708
## [113,]  2.08865072 -2.00573137  0.630966196  0.5921075370  0.177032308
## [114,]  0.55183758 -4.17156868 -2.293856130 -0.2421059723  0.444054020
## [115,]  1.35615544 -0.09187119  0.383802852  0.4500965647  0.423353296
## [116,]  1.59285208 -0.64501491  0.159500525  0.7634819166 -0.127968954
## [117,]  1.03354264  0.44766508  0.191230360 -0.7139680299 -0.153397200
## [118,]  1.10650532  1.33280785  0.827860567  0.1417263911  0.290635184
## [119,]  1.79986845 -1.40505687  0.322141350 -0.0791400991 -0.120796976
## [120,] -0.21459107 -1.33852695 -1.391473171  0.4529846546 -0.388206744
## [121,]  1.51521244  1.66387402  0.042250806 -0.6325094036 -0.300392207
## [122,]  1.43892688 -0.52584403  0.379371065  0.0620628581 -1.118821476
## [123,]  1.91327789  0.50785358  0.053139247 -0.7421947867  1.726155312
## [124,]  1.83486311  0.07159137  0.892826413  0.4519339686 -0.625017061
## [125,]  1.26881609  0.21659551  2.038510524 -1.6743249779 -1.076859518
## [126,]  2.64347168 -0.20711366 -0.357056245  0.5489262741  1.295403993
## [127,]  1.97238957 -0.04394473  0.337527277  0.6384363132  1.282533562
## [128,]  2.21146749 -0.92099559  0.941396371  0.7278300231  0.182928297
## [129,]  0.98345245 -1.61438997 -0.466739655  1.1365315716 -0.858873698
## [130,]  2.37518720  1.00413447 -0.215638919 -0.9729786728  1.654616555
## [131,]  2.06157488 -0.38843178 -0.188792818  0.5465048396 -0.560403533
## [132,]  1.22890977  1.12645653 -1.245347886 -0.6468514931  1.090057577
## [133,]  1.98155224 -1.02116536 -0.426608096  1.0168219553  0.365066781
## [134,]  2.30579180 -0.69970833  0.238917999  0.2382053163  0.661734024
## [135,]  2.37465573 -0.87923924  0.518214738  0.3430625446  0.423301845
## [136,]  2.81622514 -1.37160640  0.236038573  0.2479219275 -1.368156434
## [137,]  3.26673738  0.03810487 -0.045376385 -0.0789042527  0.963288203
## [138,]  2.38813295 -0.29412756  0.412581292  0.5524204241  0.655790520
## [139,]  2.28879123 -0.42997271  0.878680206  1.0719698515  1.135336469
## [140,]  3.02351471  1.35178692  0.332192021 -0.7621834317  1.699280417
## [141,]  3.65764812 -0.45726281 -0.429422385 -1.1794915517  0.017907334
## [142,]  0.83976623  0.36771883  0.960230241  0.9546267038  0.068322686
## [143,]  3.16815949 -0.85782974  0.648347013  0.1550807654 -1.375580499
## [144,]  2.83528246  0.14770720 -0.090942012 -0.4550997774  0.286495808
## [145,]  3.39869086 -1.20640549 -1.316955242 -1.9953016954  0.429055128
## [146,]  2.61049838  0.67610917  0.509489231 -0.0352743389  0.137672230
## [147,]  3.50623529 -0.71298832  0.119260545 -1.1451821612  0.148346569
## [148,]  3.11672979 -0.69073080 -0.221126791  0.4775553596  0.006091631
## [149,]  2.94328328 -1.07075480  0.340234851  0.1113525986  0.009353500
## [150,]  3.33086954 -0.89634966  0.838133192 -0.1659306678 -1.176304642
## [151,]  0.90890133 -2.93129847  2.696761035 -0.3454789097  0.531420637
## [152,]  2.71242548 -1.30599651 -0.934824563 -2.8828984344 -0.651856612
## [153,]  1.98262934 -0.92958240 -0.618511584  0.2775681081  0.075076935
## [154,]  4.24061565 -0.11655107  0.203365889 -1.0389509963  0.437649384
## [155,]  5.04420533 -1.54281984  0.449205665 -0.6897084890 -1.569703496
##                 PC6          PC7
##   [1,] -0.193278348  0.186116013
##   [2,] -0.172181065 -0.167388330
##   [3,] -0.148434455  0.084584687
##   [4,] -0.059863309 -0.152686457
##   [5,] -0.131868540 -0.329984556
##   [6,] -0.273250738  0.099234722
##   [7,] -0.142311156 -0.039944517
##   [8,] -0.059130911 -0.219963544
##   [9,] -0.060672088 -0.140365759
##  [10,] -0.045162961 -0.040299210
##  [11,] -0.537082655 -0.137569790
##  [12,] -0.474430284 -0.495376570
##  [13,] -0.167896822  0.013520788
##  [14,] -0.350806126  0.364060829
##  [15,]  0.013156479  0.084711002
##  [16,] -0.104216928  0.008229181
##  [17,] -0.114115888 -0.135163735
##  [18,]  0.128005879  0.326818335
##  [19,] -0.092388291  0.012645203
##  [20,] -0.551868609 -0.267821451
##  [21,] -0.196052533  0.527361820
##  [22,] -0.678138566 -0.263153265
##  [23,] -0.334650185 -0.057422210
##  [24,] -0.438445266 -0.246228590
##  [25,] -0.673123040 -0.353408820
##  [26,]  0.364472492 -0.070590320
##  [27,]  0.097452031  0.048672773
##  [28,] -0.174464087 -0.255861877
##  [29,] -0.794560353 -0.260264670
##  [30,] -0.276136008 -0.038219160
##  [31,] -0.008462204 -0.196264120
##  [32,] -0.285461377  0.214373048
##  [33,] -0.236382139  0.100714799
##  [34,]  0.117517903 -0.304710111
##  [35,]  0.168782065  0.923190698
##  [36,] -0.468588265 -0.137918913
##  [37,] -0.353711199  0.528005369
##  [38,] -0.454880796  0.797179815
##  [39,] -0.210909645  0.794175687
##  [40,] -0.264113185 -0.023098679
##  [41,] -0.053922441  0.382974407
##  [42,] -0.429221722  0.443728330
##  [43,] -0.423995421 -0.820826587
##  [44,] -0.270718033 -0.513527847
##  [45,] -0.575603297 -0.443294964
##  [46,]  0.054760571  0.075370909
##  [47,] -0.037456516 -0.411964425
##  [48,]  0.042234399 -0.152041873
##  [49,] -0.317218260  0.283495782
##  [50,] -0.664714591  0.117495964
##  [51,]  0.564339600 -0.306564081
##  [52,] -0.292703249  0.093755624
##  [53,] -0.582210864 -0.267733858
##  [54,] -0.168253394  0.088785754
##  [55,] -0.009844272 -0.101147476
##  [56,] -0.630939320 -0.662451468
##  [57,] -0.152649722  0.145044211
##  [58,] -0.367501250 -0.073342668
##  [59,] -0.154728084  0.191483215
##  [60,]  0.039953860  0.278527608
##  [61,]  0.338720944 -0.013120756
##  [62,]  0.590383391  0.122131642
##  [63,] -0.106487132 -0.138329601
##  [64,]  0.018077536  0.346762516
##  [65,]  0.187413127  0.095062523
##  [66,]  0.473302424 -0.002775801
##  [67,]  0.089170435 -0.181086011
##  [68,] -0.007236319  0.174643612
##  [69,]  0.015394869 -0.065462690
##  [70,]  0.228452326 -0.276232951
##  [71,]  0.932945889  0.068066874
##  [72,] -0.030232900 -0.029535364
##  [73,] -0.127612630 -0.137884220
##  [74,]  0.066194003 -0.193942698
##  [75,]  0.061235244  0.041635210
##  [76,]  0.301093229 -0.301690831
##  [77,] -0.127683194  0.215326533
##  [78,] -0.280845582  0.039237636
##  [79,]  0.354157646 -0.257612646
##  [80,] -0.954540549  0.043669223
##  [81,]  0.043343159  0.545412232
##  [82,]  0.099433223 -0.158946467
##  [83,]  0.092818394 -0.108885105
##  [84,] -0.240498389 -0.235426459
##  [85,]  0.137086945  0.162175842
##  [86,]  0.577179774  0.099462008
##  [87,]  0.303005372 -0.248459151
##  [88,]  0.279711377 -0.134052063
##  [89,]  0.916953061  0.082833148
##  [90,]  0.070157158 -0.157719551
##  [91,]  0.088550829 -0.467063578
##  [92,]  0.329947196  0.018053404
##  [93,] -0.818051720 -0.626250051
##  [94,]  0.674596139 -0.471114794
##  [95,] -0.595514360  0.889470731
##  [96,]  0.338744749 -0.783842741
##  [97,]  0.524972052  0.105934275
##  [98,]  0.482386426 -0.498876776
##  [99,]  0.190242444 -0.583944337
## [100,]  0.656908535  0.184009935
## [101,]  0.084266594  0.975985097
## [102,]  0.105128030 -0.040636462
## [103,]  0.184945634 -0.571351883
## [104,]  0.108465434  0.116800482
## [105,]  0.838879489 -0.058494468
## [106,] -0.864297415  0.317525070
## [107,] -0.490735969  0.450666220
## [108,]  0.109917634  0.551397073
## [109,]  0.466802080 -0.065687540
## [110,]  0.138098063 -0.427881179
## [111,]  0.299291233  0.565643543
## [112,]  0.050221774  0.160915722
## [113,] -0.452224041 -0.115792885
## [114,]  0.382968464 -0.232439335
## [115,]  0.273880805 -0.547138793
## [116,]  0.011384763  0.306459823
## [117,]  0.346023696  0.495721485
## [118,]  0.317977481  0.864099704
## [119,]  0.001168142 -0.481115748
## [120,]  1.162528253  0.319529913
## [121,]  0.463609676 -0.236681338
## [122,]  0.288804793  0.252272601
## [123,]  0.021730864 -0.097740185
## [124,]  0.073556788  0.549716149
## [125,]  0.424162557 -0.193279842
## [126,] -0.139181074 -0.721667143
## [127,]  0.103166213 -0.026038480
## [128,] -0.199117047  0.704747772
## [129,]  0.745635607  0.092715666
## [130,] -0.007590484 -0.039829069
## [131,]  0.178338082  0.435322992
## [132,]  0.833621680 -0.119180769
## [133,]  0.281298097 -0.002299740
## [134,]  0.095054122 -0.202298447
## [135,]  0.158958570 -0.673166247
## [136,] -0.064028299 -0.270447092
## [137,] -0.424775457  0.382526225
## [138,]  0.181962451 -0.125137424
## [139,]  0.087634227  0.533086419
## [140,] -0.292788765  0.921759870
## [141,] -0.523352360  0.203246680
## [142,]  1.034660251  0.924362614
## [143,] -0.178776967  0.336050395
## [144,]  0.228037399 -0.595354299
## [145,] -0.307565614 -0.034378003
## [146,]  0.321276399  0.054379283
## [147,] -0.345268628  0.185689232
## [148,]  0.135651433 -0.441236917
## [149,]  0.112140708 -0.158756031
## [150,] -0.060479244 -0.028784797
## [151,]  0.997072472 -0.567340336
## [152,]  0.177626412  0.329349742
## [153,]  0.803875721  0.005725216
## [154,] -0.416996392 -0.419279115
## [155,] -0.968476275  0.168449208
happiness_new
##     Happiness.Rank    Economy    Family Life.Expectancy    Freedom
## 1                1 1.61646318 1.5335236     0.796666503 0.63542259
## 2                2 1.48238301 1.5511216     0.792565525 0.62600672
## 3                3 1.48063302 1.6105740     0.833552122 0.62716264
## 4                4 1.56497955 1.5169117     0.858131289 0.62007058
## 5                5 1.44357193 1.5402467     0.809157670 0.61795086
## 6                6 1.50394464 1.4289392     0.810696125 0.58538449
## 7                7 1.47920442 1.4813490     0.834557652 0.61110091
## 8                8 1.40570605 1.5481951     0.816759706 0.61406213
## 9                9 1.49438727 1.4781622     0.830875158 0.61292410
## 10              10 1.48441494 1.5100420     0.843886793 0.60160738
## 11              11 1.37538242 1.3762900     0.838404000 0.40598860
## 12              12 1.10970628 1.4164037     0.759509265 0.58013165
## 13              13 1.48709726 1.4599450     0.815328419 0.56776619
## 14              14 1.54625928 1.4199206     0.774286628 0.50574052
## 15              15 1.53570664 1.5582311     0.809782624 0.57311034
## 16              16 1.48792338 1.4725204     0.798950732 0.56251138
## 17              17 1.46378076 1.4623127     0.818091869 0.53977072
## 18              18 1.74194360 1.4575837     0.845089495 0.59662789
## 19              19 1.44163394 1.4964601     0.805335939 0.50819004
## 20              20 1.25278461 1.2840250     0.819479704 0.37689528
## 21              21 1.62634337 1.2664102     0.726798236 0.60834527
## 22              22 1.10735321 1.4313060     0.616552353 0.43745375
## 23              23 1.35268235 1.4338852     0.754444003 0.49094617
## 24              24 1.18529546 1.4404511     0.695137084 0.49451920
## 25              25 1.15318382 1.2108622     0.709978998 0.41273001
## 26              26 1.69227767 1.3538144     0.949492395 0.54984057
## 27              27 1.34327984 1.4884117     0.821944237 0.58876705
## 28              28 1.21755970 1.4122279     0.719216824 0.57939225
## 29              29 0.87200195 1.2555852     0.540239990 0.53131062
## 30              30 1.23374844 1.3731925     0.706156135 0.55002683
## 31              31 1.43092346 1.3877769     0.844465852 0.47022212
## 32              32 1.12786877 1.4257925     0.647239029 0.58020073
## 33              33 1.43362653 1.3845654     0.793984234 0.36146659
## 34              34 1.38439786 1.5320909     0.888960600 0.40878123
## 35              35 1.87076569 1.2742969     0.710098088 0.60413098
## 36              36 1.07062232 1.4021829     0.595027924 0.47748742
## 37              37 1.53062356 1.2866776     0.590148330 0.44975057
## 38              38 1.36135590 1.3802285     0.519983292 0.51863074
## 39              39 1.63295245 1.2596987     0.632105708 0.49633759
## 40              40 1.32539356 1.5050592     0.712732911 0.29581746
## 41              41 1.48841226 1.3231105     0.653133035 0.53674692
## 42              42 1.29121542 1.2846460     0.618784428 0.40226498
## 43              43 0.73729920 1.2872157     0.653095961 0.44755185
## 44              44 1.00082040 1.2861688     0.685636222 0.45519820
## 45              45 0.90978450 1.1821251     0.596018553 0.43245253
## 46              46 1.29178786 1.4457120     0.699475348 0.52034211
## 47              47 0.78644109 1.5489691     0.498272628 0.65824866
## 48              48 1.39506662 1.4449233     0.853144348 0.25645071
## 49              49 1.28177810 1.4692824     0.547349334 0.37378311
## 50              50 0.90797532 1.0814178     0.450191766 0.54750937
## 51              51 1.41691518 1.4363378     0.913475871 0.50562555
## 52              52 1.31458235 1.4735161     0.628949940 0.23423178
## 53              53 1.09186447 1.1462175     0.617584646 0.23333581
## 54              54 1.26074862 1.4047149     0.638566971 0.32570791
## 55              55 1.40167844 1.1282744     0.900214076 0.25792167
## 56              56 0.72887063 1.2518256     0.589465201 0.24072905
## 57              57 1.21768391 1.1500913     0.685158312 0.45700374
## 58              58 0.83375657 1.2276191     0.473630250 0.55873293
## 59              59 1.13077676 1.4931492     0.437726080 0.41827193
## 60              60 1.28455627 1.3843690     0.606041551 0.43745428
## 61              61 1.34691131 1.1863034     0.834647238 0.47120363
## 62              62 1.34120595 1.4525188     0.790828228 0.57257581
## 63              63 1.03522527 1.2187704     0.630166113 0.45000288
## 64              64 1.18939555 1.2095610     0.638007462 0.49124733
## 65              65 1.35593808 1.1313633     0.844714701 0.35511154
## 66              66 1.32087934 1.4766711     0.695168316 0.47913143
## 67              67 1.15655756 1.4449452     0.637714267 0.29540026
## 68              68 1.10180306 1.3575643     0.520169020 0.46573323
## 69              69 1.19827437 1.3377532     0.637605608 0.30074060
## 70              70 0.93253732 1.5072849     0.579250693 0.47350779
## 71              71 1.55167484 1.2627909     0.943062425 0.49096864
## 72              72 0.85769922 1.2539176     0.468009055 0.58521467
## 73              73 1.06931758 1.2581898     0.650784671 0.20871553
## 74              74 0.99101239 1.2390889     0.604590058 0.41842115
## 75              75 1.28601193 1.3431331     0.687763453 0.17586352
## 76              76 0.92557931 1.3682181     0.641022384 0.47430724
## 77              77 1.22255623 0.9679830     0.701288521 0.25577229
## 78              78 0.95148438 1.1378535     0.541452050 0.26028794
## 79              79 1.08116579 1.1608374     0.741415501 0.47278771
## 80              80 0.72688353 0.6726907     0.402047783 0.23521526
## 81              81 0.99553859 1.2744447     0.492345721 0.44332346
## 82              82 1.12843120 1.4313376     0.617144227 0.15399712
## 83              83 1.12112904 1.2383765     0.667464674 0.19498906
## 84              84 0.87811458 0.7748644     0.597710669 0.40815833
## 85              85 1.15360177 1.1524003     0.540775776 0.39815584
## 86              86 1.07937384 1.4024167     0.574873745 0.55258983
## 87              87 1.28948748 1.2394146     0.810198903 0.09573125
## 88              88 1.07498753 1.1296242     0.735081077 0.28851599
## 89              89 1.31517529 1.3670430     0.795843542 0.49846530
## 90              90 0.98240942 1.0693359     0.705186307 0.20440318
## 91              91 0.73057312 1.1439450     0.582569480 0.34807986
## 92              92 1.06457794 1.2078930     0.644948184 0.32590598
## 93              93 0.02264318 0.7211514     0.113989137 0.60212696
## 94              94 0.78854758 1.2774913     0.652168989 0.57105559
## 95              95 0.78375626 1.2157705     0.056915730 0.39495257
## 96              96 0.52471364 1.2714633     0.529235125 0.47156671
## 97              97 0.88541639 1.3401265     0.495879292 0.50153768
## 98              98 0.59622008 1.3942386     0.553457797 0.45494339
## 99              99 0.47982019 1.1792833     0.504130781 0.44030595
## 100            100 1.02723587 1.4930112     0.557783484 0.39414397
## 101            101 1.05469871 1.3847886     0.187080070 0.47924674
## 102            102 1.00726581 0.8683515     0.613212049 0.28968069
## 103            103 0.71624923 1.1556472     0.565666974 0.25471106
## 104            104 0.98970181 0.9974714     0.520187259 0.28211015
## 105            105 1.16145909 1.4343795     0.708217680 0.28923172
## 106            106 0.36842093 0.9841360     0.005564754 0.31869769
## 107            107 0.56430537 0.9460182     0.132892117 0.43038875
## 108            108 1.15687311 0.7115512     0.639333189 0.24932261
## 109            109 0.99619275 0.8036852     0.731159747 0.38149863
## 110            110 0.58668298 0.7351317     0.533241034 0.47835666
## 111            111 0.96443433 1.0984708     0.338611811 0.52030355
## 112            112 0.56047946 1.0679507     0.309988350 0.45276377
## 113            113 0.23430565 0.8707010     0.106654435 0.48079109
## 114            114 0.36711055 1.1232359     0.397522569 0.51449203
## 115            115 0.47930902 1.1796919     0.409362853 0.37792227
## 116            116 0.63640678 1.0031873     0.257835895 0.46160349
## 117            117 1.10271049 0.9786132     0.501180470 0.28855553
## 118            118 1.19821024 1.1556202     0.356578588 0.31232858
## 119            119 0.33923385 0.8646692     0.353409708 0.40884274
## 120            120 1.00985014 1.2599764     0.625130832 0.56121325
## 121            121 0.90059674 1.0074837     0.637524426 0.19830327
## 122            122 0.79222125 0.7543726     0.455427617 0.46998700
## 123            123 0.64845729 1.2720308     0.285349280 0.09609804
## 124            124 0.80896425 0.8320444     0.289957434 0.43502587
## 125            125 0.95061266 0.5706149     0.649546981 0.30941004
## 126            126 0.09210235 1.2290235     0.191407025 0.23596135
## 127            127 0.47618049 1.2814734     0.169365674 0.30661374
## 128            128 0.60304892 0.9047800     0.048642170 0.44770619
## 129            129 0.60176510 1.0062383     0.429783404 0.63337582
## 130            130 0.65951669 1.2140086     0.290920824 0.01499586
## 131            131 0.66722482 0.8736647     0.295637727 0.42302629
## 132            132 0.89465195 1.3945376     0.575903952 0.12297478
## 133            133 0.38143072 1.1298277     0.217632607 0.44318596
## 134            134 0.35022771 1.0432800     0.215844259 0.32436785
## 135            135 0.16192533 0.9930250     0.268505007 0.36365870
## 136            136 0.23344204 0.5125688     0.315089583 0.46691465
## 137            137 0.43801299 0.9538559     0.041134715 0.16234203
## 138            138 0.37584653 1.0830959     0.196763754 0.33638421
## 139            139 0.52102125 1.1900952     0.000000000 0.39066130
## 140            140 0.85842818 1.1044120     0.049868666 0.00000000
## 141            141 0.40147722 0.5815433     0.180746779 0.10617952
## 142            142 1.12209415 1.2215550     0.341755509 0.50519633
## 143            143 0.43108541 0.4352998     0.209930211 0.42596278
## 144            144 0.30580869 0.9130204     0.375223309 0.18919677
## 145            145 0.36861026 0.6404498     0.277321130 0.03036986
## 146            146 0.59168345 0.9353822     0.310080916 0.24946372
## 147            147 0.39724863 0.6013231     0.163486004 0.14706244
## 148            148 0.11904179 0.8721179     0.229918197 0.33288118
## 149            149 0.24454993 0.7912447     0.194129139 0.34858751
## 150            150 0.30544472 0.4318825     0.247105569 0.38042614
## 151            151 0.36874589 0.9457070     0.326424807 0.58184385
## 152            152 0.77715313 0.3961026     0.500533342 0.08153944
## 153            153 0.51113588 1.0419898     0.364509284 0.39001778
## 154            154 0.09162257 0.6297936     0.151610792 0.05990075
## 155            155 0.00000000 0.0000000     0.018772686 0.27084205
##     Generosity       Trust
## 1   0.36201224 0.315963835
## 2   0.35528049 0.400770068
## 3   0.47554022 0.153526559
## 4   0.29054928 0.367007285
## 5   0.24548277 0.382611543
## 6   0.47048983 0.282661825
## 7   0.43553972 0.287371516
## 8   0.50000513 0.382816702
## 9   0.38539925 0.384398729
## 10  0.47769925 0.301183730
## 11  0.33008265 0.085242100
## 12  0.21461323 0.100106589
## 13  0.31647232 0.221060365
## 14  0.39257878 0.135638788
## 15  0.42785832 0.298388153
## 16  0.33626917 0.276731938
## 17  0.23150334 0.251343131
## 18  0.28318098 0.318834424
## 19  0.49277416 0.265428066
## 20  0.32666242 0.082287982
## 21  0.36094195 0.324489564
## 22  0.16234989 0.111092761
## 23  0.08810676 0.036872927
## 24  0.10945706 0.059739888
## 25  0.12099043 0.132774115
## 26  0.34596598 0.464307785
## 27  0.57473058 0.153066069
## 28  0.17509693 0.178061873
## 29  0.28348839 0.077223279
## 30  0.21055694 0.070983924
## 31  0.12976231 0.172502428
## 32  0.57212311 0.031612735
## 33  0.25836048 0.063829236
## 34  0.19013357 0.070914097
## 35  0.33047387 0.439299256
## 36  0.14901447 0.046668742
## 37  0.14761601 0.273432255
## 38  0.32529646 0.008964816
## 39  0.22828980 0.215159550
## 40  0.13654448 0.024210852
## 41  0.17266849 0.257042170
## 42  0.41660893 0.065600708
## 43  0.30167422 0.130687982
## 44  0.15011247 0.140134647
## 45  0.07825799 0.089980960
## 46  0.15846597 0.059307806
## 47  0.41598365 0.246528223
## 48  0.17278965 0.028028091
## 49  0.05226382 0.032962881
## 50  0.24001564 0.096581072
## 51  0.12057277 0.163760737
## 52  0.01016466 0.011865643
## 53  0.06943665 0.146096110
## 54  0.15307479 0.073842727
## 55  0.20667437 0.063282669
## 56  0.20877913 0.010091286
## 57  0.13351992 0.004387901
## 58  0.22556072 0.060477726
## 59  0.24992499 0.259270340
## 60  0.20196442 0.119282886
## 61  0.26684570 0.155353352
## 62  0.24264909 0.045128979
## 63  0.12681971 0.047049087
## 64  0.36093375 0.042181555
## 65  0.27125430 0.041237976
## 66  0.09889081 0.183248922
## 67  0.15513751 0.156313822
## 68  0.15207367 0.092610210
## 69  0.04669304 0.099671580
## 70  0.22415066 0.091065913
## 71  0.37446579 0.293933749
## 72  0.19351342 0.099331893
## 73  0.22012588 0.040903781
## 74  0.17217046 0.119803272
## 75  0.07840166 0.036636937
## 76  0.23381834 0.055267781
## 77  0.24800298 0.043103110
## 78  0.31993145 0.057471618
## 79  0.02880684 0.022794275
## 80  0.31544602 0.124348067
## 81  0.61170459 0.015317135
## 82  0.06501963 0.064491123
## 83  0.19791102 0.088174194
## 84  0.03220996 0.087763183
## 85  0.04526934 0.180987507
## 86  0.18696785 0.113945253
## 87  0.00000000 0.043289777
## 88  0.26445076 0.037513830
## 89  0.09510271 0.015869452
## 90  0.32886750 0.000000000
## 91  0.23618887 0.073345453
## 92  0.25376096 0.060277794
## 93  0.29163131 0.282410324
## 94  0.23496805 0.087633237
## 95  0.23094720 0.026121566
## 96  0.24899764 0.146377146
## 97  0.47405455 0.173380390
## 98  0.42858037 0.039439179
## 99  0.39409617 0.072975546
## 100 0.33846423 0.032902289
## 101 0.13936238 0.072509497
## 102 0.04969336 0.086723149
## 103 0.11417317 0.089282602
## 104 0.12863144 0.114381365
## 105 0.11317769 0.011051531
## 106 0.29304090 0.071095176
## 107 0.23629846 0.051306631
## 108 0.38724291 0.048761073
## 109 0.20131294 0.039864216
## 110 0.17225535 0.123717859
## 111 0.07713374 0.093146972
## 112 0.44486031 0.064641319
## 113 0.32222810 0.179436386
## 114 0.83807516 0.188816205
## 115 0.18346889 0.115460448
## 116 0.24958014 0.078213550
## 117 0.19963726 0.107215755
## 118 0.04378538 0.076046787
## 119 0.31265074 0.165455714
## 120 0.49086356 0.073653966
## 121 0.08348809 0.026674422
## 122 0.23153849 0.092226885
## 123 0.20187002 0.136957005
## 124 0.12085213 0.079618134
## 125 0.05400882 0.251666635
## 126 0.24645583 0.060241356
## 127 0.18335420 0.104970247
## 128 0.20123747 0.130061775
## 129 0.38592297 0.068105951
## 130 0.18231745 0.089847520
## 131 0.25692394 0.025336370
## 132 0.27006146 0.023029471
## 133 0.32576606 0.057069719
## 134 0.25086468 0.120328106
## 135 0.22867385 0.138572946
## 136 0.28717047 0.072711654
## 137 0.21611385 0.053581882
## 138 0.18914349 0.095375381
## 139 0.15749727 0.119094640
## 140 0.09792649 0.069720335
## 141 0.31187093 0.061157830
## 142 0.09934845 0.098583199
## 143 0.20794846 0.060929015
## 144 0.20873253 0.067231975
## 145 0.48920378 0.099872150
## 146 0.10412521 0.056767423
## 147 0.28567082 0.116793513
## 148 0.26654989 0.038948249
## 149 0.26481509 0.110937618
## 150 0.19689615 0.095665015
## 151 0.25275603 0.455220014
## 152 0.49366373 0.151347131
## 153 0.35425636 0.066035107
## 154 0.20443518 0.084147945
## 155 0.28087649 0.056565076
md.pattern(happiness_new)
##  /\     /\
## {  `---'  }
## {  O   O  }
## ==>  V <==  No need for mice. This data set is completely observed.
##  \  \|/  /
##   `-----'

##     Happiness.Rank Economy Family Life.Expectancy Freedom Generosity Trust
## 155              1       1      1               1       1          1     1
##                  0       0      0               0       0          0     0
##      
## 155 0
##     0
happy.pca <- PCA(happiness_new, graph = F)
eig.val <- get_eigenvalue(happy.pca)
eig.val
##       eigenvalue variance.percent cumulative.variance.percent
## Dim.1  3.7738458        53.912084                    53.91208
## Dim.2  1.3526400        19.323429                    73.23551
## Dim.3  0.6725747         9.608209                    82.84372
## Dim.4  0.5434929         7.764184                    90.60791
## Dim.5  0.3613563         5.162233                    95.77014
## Dim.6  0.1613830         2.305471                    98.07561
## Dim.7  0.1347073         1.924390                   100.00000
fviz_eig(happy.pca, addlabels = TRUE, ylim = c(0, 60), linecolor = "purple", barfill = "orange", barcolor = "orange")

#Showing the variables
var <- get_pca_var(happy.pca)
fviz_pca_var(happy.pca, col.var = "darkblue")

#Analysis:We see that for instance family, life expectancy and economy are highly correlated. Trust in the government and freedom are also correlated.
#We also see that life expectancy, etc are more correlated with the first dimension whereas freedom, generousity are more correlated with the second dimension.

#Here ,Cos2 shows the quality of representation
fviz_cos2(happy.pca, choice ="var", axes = 1:2, top = 10, color = "dark blue" )

#Contribution of the variables
var$contrib
##                     Dim.1      Dim.2        Dim.3      Dim.4      Dim.5
## Happiness.Rank  22.932697  0.6789814  0.310774650  0.1793912  0.2806790
## Economy         20.716212  6.7440632  0.001856379  6.5004663  1.0187508
## Family          17.085010  3.5540115  3.460364349 14.4828341 53.2314125
## Life.Expectancy 19.113500  5.4852433  2.941657344 11.0780005 16.6241839
## Freedom         11.316840 16.4889758  4.792429952 44.9297245 18.6687593
## Generosity       1.107816 44.8927351 48.343342584  4.2587796  0.3446663
## Trust            7.727925 22.1559896 40.149574744 18.5708037  9.8315482
#Contribution of the top 5 variables
fviz_contrib(happy.pca, choice = "var", axes = 1, top = 5)

#PCA plot with "fviz_pca_ind"
ind <- get_pca_ind(happy.pca)
ind
## Principal Component Analysis Results for individuals
##  ===================================================
##   Name       Description                       
## 1 "$coord"   "Coordinates for the individuals" 
## 2 "$cos2"    "Cos2 for the individuals"        
## 3 "$contrib" "contributions of the individuals"
happy.pca$ind
## $coord
##           Dim.1       Dim.2        Dim.3         Dim.4        Dim.5
## 1    3.59019076  1.08250314 -0.450468480  0.1795351775  0.203717981
## 2    3.65800154  1.49462921 -0.993233171  0.4623440892  0.575769809
## 3    3.22559015  0.87222222  1.240646388 -0.4309720952 -0.057690812
## 4    3.64145132  0.90892328 -1.086410048  0.4358566269  0.242359558
## 5    3.44481314  0.86107541 -1.434504080  0.2696471323  0.451434484
## 6    3.17060061  1.46674977  0.329620821  0.5239736659  0.034982403
## 7    3.29625427  1.34316769  0.132010313  0.3248831025  0.059475006
## 8    3.58837220  2.13705235 -0.105495526  0.6588928161  0.594333348
## 9    3.51091218  1.54878811 -0.744266994  0.6624902662  0.329475308
## 10   3.37804136  1.56596767  0.299356612  0.4719575501  0.207386088
## 11   1.89759657 -0.54176346  1.081099812  0.3085747334 -0.263923728
## 12   1.85267489 -0.35751917  0.105847457 -0.9112901797 -0.468185291
## 13   2.80142571  0.36293579 -0.041535574  0.0660987685 -0.088667493
## 14   2.40693880  0.20949050  0.919669437  0.1301033239 -0.184706513
## 15   3.27581897  1.20653752  0.100411327  0.4350945432  0.434518627
## 16   2.91448110  0.71896509 -0.286965843  0.3203839472  0.171089074
## 17   2.69536078  0.02130748 -0.630856935  0.1802297838  0.060421297
## 18   3.38319960  0.55293877 -0.856260913  0.5060763390  0.003147218
## 19   2.84789300  1.31155876  0.690162219  0.7081314382  0.426159863
## 20   1.42378839 -0.47957501  1.040839045  0.4511586520 -0.352769036
## 21   2.83362777  1.31905581 -0.718936916  0.6170872714 -0.230884041
## 22   1.16828884 -0.80289677 -0.130939210 -0.5183208187  0.252331636
## 23   1.54122275 -1.66106879 -0.031409762 -0.8469552295 -0.453559231
## 24   1.33613264 -1.27872740 -0.107438985 -0.9259930732 -0.223273469
## 25   1.01198728 -0.94591337 -0.523979955  0.0764714232 -0.357585894
## 26   3.63004496  1.42506958 -1.373565987  1.6932889379  0.192808428
## 27   2.61190764  1.48167383  1.696817921 -0.0231876923 -0.134589630
## 28   1.89913318 -0.18912330 -0.638043896 -0.6076488590 -0.190227267
## 29   0.65629739  0.24877686  0.394518179 -0.9074308031 -0.321921960
## 30   1.48273620 -0.55711397  0.220542511 -0.8316819353 -0.499657963
## 31   1.99858112 -0.97216166 -0.608968162  0.1299486787 -0.237954031
## 32   1.55651509  1.23819752  2.283822065 -0.7943503891 -0.287148669
## 33   1.43999584 -1.07681933  0.854118746  0.2896065082 -0.123098722
## 34   1.83699125 -1.41461515  0.551470094 -0.0884670870 -0.042663663
## 35   3.21112656  1.57476951 -1.615316132  1.2052585457  0.129905942
## 36   0.79961818 -0.97090225  0.092666509 -0.9929479454 -0.080189189
## 37   1.67091491 -0.20194648 -1.376479078  0.5208289418  0.305625401
## 38   1.05041773 -0.24261883  1.106317449 -0.9648583853 -0.232773877
## 39   1.80739380 -0.02725394 -0.653403410  0.3471244804 -0.137459329
## 40   0.91940768 -1.96558485  0.579225235 -0.1054372912  0.372346671
## 41   1.92182469  0.03022989 -1.206879543  0.1180054614  0.013500606
## 42   0.94139405  0.17562741  1.400484941  0.1653047760 -0.073746850
## 43   0.58720294  0.33684909  0.363360121 -0.2283281853  0.028379113
## 44   0.84553195 -0.54726939 -0.472320112 -0.2468923543 -0.151762295
## 45   0.17498422 -0.98484733 -0.630228823 -0.5109193462 -0.360012099
## 46   1.32716611 -0.99936145  0.089157889 -0.8836401621 -0.271476494
## 47   1.57055502  1.97532852 -0.025706409 -1.0369986248  0.754898419
## 48   1.03264958 -2.00198008  0.852741423  0.4705813909  0.110970370
## 49   0.55153578 -1.90246773 -0.179892746 -0.7506017114  0.352632688
## 50   0.10805231  0.38619782 -0.180277345 -0.8181117234 -0.600042858
## 51   2.01557730 -1.01730916 -0.596841842 -0.0369891189 -0.339613557
## 52   0.30725355 -2.68788968 -0.003619227 -0.1472664138  0.537891440
## 53  -0.02596917 -1.40470271 -0.754741449  0.8032080963  0.222145063
## 54   0.63371570 -1.36603557  0.174861070 -0.0001182077  0.351183754
## 55   0.72146144 -1.49460330  0.625166290  1.1637615643 -0.650742770
## 56  -0.59886345 -1.13282753  0.854230826  0.0079345669  0.249348140
## 57   0.36306699 -1.27594459  0.180820513 -0.5334538325 -0.966976444
## 58   0.11118797  0.11808167  0.057023393 -1.2430147777 -0.391088012
## 59   0.98809102  0.46023239 -0.712133983  0.0475521644  1.308845639
## 60   0.92018099 -0.56569531 -0.064418078 -0.2313746977  0.195873186
## 61   1.33988909 -0.11583277  0.031765355  0.4944683889 -0.674637051
## 62   1.53296811 -0.59966697  0.587307389 -0.8839908259 -0.562736198
## 63   0.19428962 -0.99690256 -0.111265417 -0.6049007583 -0.496771261
## 64   0.61496296  0.16478333  1.067303217 -0.3329379273 -0.601628775
## 65   0.67537507 -0.91125355  0.904673167  0.6328767438 -0.845123953
## 66   1.38202010 -0.82997824 -0.942071909 -0.2739072679  0.308093148
## 67   0.59768548 -0.99216335 -0.275394056  0.3839543573  0.839307685
## 68   0.39004461 -0.63096855 -0.285075293 -0.7370666156  0.143131302
## 69   0.23827472 -1.73308077 -0.562910988  0.1221116786  0.320601101
## 70   0.58012107 -0.30620288  0.225624200 -0.8845443934  0.470039843
## 71   2.27501840  0.85213361 -0.192929520  1.3454980008 -0.285515873
## 72   0.15596259  0.20987190 -0.395251427 -1.2607935887 -0.273870467
## 73  -0.26734908 -1.26450200  0.791119462  0.6008736382  0.290783210
## 74   0.17465669 -0.45894151 -0.303864502 -0.1637372222 -0.040995484
## 75  -0.05922970 -2.30342932  0.209723011  0.5851090777  0.413444889
## 76   0.33305227 -0.37640721  0.445420204 -0.7522311431 -0.090970228
## 77  -0.33561271 -0.93394744  0.694304747  0.9958628220 -0.684961972
## 78  -0.58503514 -0.27995899  0.965993861  0.5324832729  0.152921854
## 79   0.10302849 -1.60760482 -0.477517680 -0.6839941410 -1.012178559
## 80  -1.65602850  0.52739577  0.157744941  1.2096633100 -0.457961318
## 81   0.06176493  1.41349229  2.520548688 -0.2402683389  0.046758710
## 82  -0.29174244 -2.17779490 -0.002747785  0.4751808220  0.949784769
## 83  -0.23450389 -1.21014718  0.386903959  0.9201041371  0.387119629
## 84  -0.95838569 -0.93289491 -1.130727040  0.0791640263 -1.303635609
## 85   0.01299175 -0.82275556 -1.431560663  0.1269630978  0.014553475
## 86   0.62030780 -0.15865229 -0.318419754 -0.9612859596  0.020000636
## 87  -0.33056872 -2.91428572 -0.113815543  1.1759902308  0.169181103
## 88  -0.24639433 -0.81724080  0.883785712  0.6040338150 -0.387593203
## 89   0.73902145 -1.55557469  0.029901743 -0.7724142322 -0.691798568
## 90  -0.75230087 -0.76834996  1.512499457  0.9041833642 -0.309837879
## 91  -0.70355749 -0.26948040  0.324657205  0.0056018431 -0.075132925
## 92  -0.21606758 -0.61165227  0.611922817  0.2832764736 -0.067686757
## 93  -1.78183493  2.85291721 -1.677666609 -0.6855800166 -0.234240846
## 94   0.18873097  0.20910954  0.035457933 -0.9765413632 -0.466031403
## 95  -1.58567683  0.06044866  0.183802618 -1.2131475637  0.722920611
## 96  -0.40682022  0.57459457 -0.206514900 -0.5827616046  0.271992182
## 97   0.32914003  1.67022062  0.760963635 -0.1746129815  0.514179970
## 98  -0.31866122  0.78290935  1.510051896 -0.7708578001  0.323815030
## 99  -0.82518425  0.99127696  0.967807729 -0.4691773930  0.021402880
## 100  0.05290554 -0.19452024  1.233948258 -0.5257994763  0.582773232
## 101 -0.62590514 -0.34889118 -0.509659696 -1.4053914310  0.730822047
## 102 -1.10298496 -1.29603823 -0.812031993  0.6258845868 -0.755129270
## 103 -1.12386552 -1.01754880 -0.289242696  0.2678328590  0.267323039
## 104 -1.00847591 -0.77268979 -0.551301795  0.5872857485 -0.117065699
## 105 -0.13377534 -1.88892050  0.421724839 -0.1183105031  0.284555941
## 106 -2.58304007  0.85274993  0.138400640 -0.5911599516  0.720939804
## 107 -2.04807118  0.56064248 -0.130672107 -0.9131920434 -0.050635399
## 108 -1.11337347  0.09724649  1.139486494  1.5057013211 -1.081961475
## 109 -0.86822829 -0.56273437  0.166226661  0.4998416173 -1.456540265
## 110 -1.36323472  0.44160282 -0.835547400 -0.0580565710 -1.223134218
## 111 -0.86360996 -0.33924507 -1.108442136 -1.0487126053 -0.308611136
## 112 -1.35175113  1.47929232  1.034144743 -0.5466342237  0.029073645
## 113 -2.09542108  2.01223295 -0.633011472 -0.5940268534  0.177606159
## 114 -0.55362636  4.18509082  2.301291664  0.2428907587  0.445493421
## 115 -1.36055142  0.09216899 -0.385046949 -0.4515555525  0.424725596
## 116 -1.59801531  0.64710572 -0.160017546 -0.7659567429 -0.128383765
## 117 -1.03689287 -0.44911619 -0.191850233  0.7162823570 -0.153894437
## 118 -1.11009206 -1.33712815 -0.830544077 -0.1421857971  0.291577277
## 119 -1.80570273  1.40961136 -0.323185571  0.0793966317 -0.121188539
## 120  0.21528667  1.34286579  1.395983630 -0.4544530041 -0.389465116
## 121 -1.52012400 -1.66926747 -0.042387762  0.6345596825 -0.301365928
## 122 -1.44359116  0.52754855 -0.380600796 -0.0622640349 -1.122448135
## 123 -1.91947978 -0.50949979 -0.053311498  0.7446006108  1.731750643
## 124 -1.84081082 -0.07182344 -0.895720510 -0.4533989124 -0.627043054
## 125 -1.27292896 -0.21729760 -2.045118355  1.6797523017 -1.080350157
## 126 -2.65204050  0.20778502  0.358213643 -0.5507056183  1.299603045
## 127 -1.97878307  0.04408718 -0.338621371 -0.6405058043  1.286690894
## 128 -2.21863596  0.92398100 -0.944447907 -0.7301892838  0.183521259
## 129 -0.98664032  1.61962302  0.468252592 -1.1402156382 -0.861657736
## 130 -2.38288637 -1.00738937  0.216337912  0.9761325828  1.659979994
## 131 -2.06825748  0.38969089  0.189404790 -0.5482763348 -0.562220081
## 132 -1.23289328 -1.13010794  1.249384681  0.6489482620  1.093590998
## 133 -1.98797544  1.02447547  0.427990946 -1.0201179832  0.366250144
## 134 -2.31326602  0.70197644 -0.239692452 -0.2389774587  0.663879034
## 135 -2.38235317  0.88208929 -0.519894531 -0.3441745817  0.424673978
## 136 -2.82535393  1.37605246 -0.236803692 -0.2487255663 -1.372591312
## 137 -3.27732651 -0.03822839  0.045523472  0.0791600208  0.966410701
## 138 -2.39587408  0.29508097 -0.413918674 -0.5542110947  0.657916265
## 139 -2.29621034  0.43136647 -0.881528448 -1.0754446414  1.139016662
## 140 -3.03331543 -1.35616873 -0.333268822  0.7646540489  1.704788634
## 141 -3.66950439  0.45874502  0.430814357  1.1833148731  0.017965380
## 142 -0.84248834 -0.36891079 -0.963342827 -0.9577211259  0.068544153
## 143 -3.17842908  0.86061040 -0.650448629 -0.1555834596 -1.380039443
## 144 -2.84447303 -0.14818599  0.091236800  0.4565749832  0.287424484
## 145 -3.40970772  1.21031606  1.321224151  2.0017694650  0.430445911
## 146 -2.61896031 -0.67830078 -0.511140740  0.0353886807  0.138118495
## 147 -3.51760075  0.71529947 -0.119647128  1.1488942687  0.148827434
## 148 -3.12683266  0.69296980  0.221843574 -0.4791033551  0.006111377
## 149 -2.95282393  1.07422565 -0.341337722 -0.1117135480  0.009383819
## 150 -3.34166655  0.89925518 -0.840850001  0.1664685320 -1.180117633
## 151 -0.91184753  2.94080028 -2.705502585  0.3465987794  0.533143237
## 152 -2.72121780  1.31022990  0.937854796  2.8922433485 -0.653969605
## 153 -1.98905603  0.93259564  0.620516489 -0.2784678450  0.075320297
## 154 -4.25436160  0.11692887 -0.204025100  1.0423187556  0.439068024
## 155 -5.06055612  1.54782089 -0.450661765  0.6919441788 -1.574791689
## 
## $cos2
##            Dim.1        Dim.2        Dim.3        Dim.4        Dim.5
## 1   8.944560e-01 8.131717e-02 1.408162e-02 2.236779e-03 2.879935e-03
## 2   7.777504e-01 1.298433e-01 5.733963e-02 1.242462e-02 1.926860e-02
## 3   8.051190e-01 5.887027e-02 1.191071e-01 1.437276e-02 2.575461e-04
## 4   8.531622e-01 5.315413e-02 7.593992e-02 1.222278e-02 3.779226e-03
## 5   7.874624e-01 4.920179e-02 1.365533e-01 4.824920e-03 1.352346e-02
## 6   7.932040e-01 1.697516e-01 8.572966e-03 2.166311e-02 9.656085e-05
## 7   8.476659e-01 1.407485e-01 1.359562e-03 8.234506e-03 2.759639e-04
## 8   7.038552e-01 2.496426e-01 6.083541e-04 2.373110e-02 1.930850e-02
## 9   7.776893e-01 1.513388e-01 3.494811e-02 2.769014e-02 6.848760e-03
## 10  8.023333e-01 1.724211e-01 6.300900e-03 1.566141e-02 3.024016e-03
## 11  6.502810e-01 5.300449e-02 2.110692e-01 1.719547e-02 1.257912e-02
## 12  6.737322e-01 2.508924e-02 2.199124e-03 1.630055e-01 4.302535e-02
## 13  9.782810e-01 1.641969e-02 2.150531e-04 5.446182e-04 9.800184e-04
## 14  8.286471e-01 6.277232e-03 1.209771e-01 2.421120e-03 4.879822e-03
## 15  8.528629e-01 1.156967e-01 8.013179e-04 1.504550e-02 1.500570e-02
## 16  9.196463e-01 5.596467e-02 8.915768e-03 1.111322e-02 3.169151e-03
## 17  9.397153e-01 5.872547e-05 5.147831e-02 4.201609e-03 4.722180e-04
## 18  8.896981e-01 2.376520e-02 5.699013e-02 1.990763e-02 7.699119e-07
## 19  7.373968e-01 1.563974e-01 4.330682e-02 4.559127e-02 1.651198e-02
## 20  5.008789e-01 5.682703e-02 2.676757e-01 5.029218e-02 3.074842e-02
## 21  7.273753e-01 1.576156e-01 4.682249e-02 3.449579e-02 4.829040e-03
## 22  4.720265e-01 2.229387e-01 5.929327e-03 9.291017e-02 2.201960e-02
## 23  3.847005e-01 4.468555e-01 1.597796e-04 1.161750e-01 3.331654e-02
## 24  3.886254e-01 3.559492e-01 2.512788e-03 1.866584e-01 1.085191e-02
## 25  3.520654e-01 3.075926e-01 9.438491e-02 2.010350e-03 4.395760e-02
## 26  6.543511e-01 1.008461e-01 9.368841e-02 1.423800e-01 1.846030e-03
## 27  5.719749e-01 1.840630e-01 2.413970e-01 4.507922e-05 1.518744e-03
## 28  7.924174e-01 7.858384e-03 8.944258e-02 8.112385e-02 7.950395e-03
## 29  1.890039e-01 2.715747e-02 6.829727e-02 3.613236e-01 4.547477e-02
## 30  6.146074e-01 8.676771e-02 1.359736e-02 1.933680e-01 6.979361e-02
## 31  7.366049e-01 1.742882e-01 6.838805e-02 3.114116e-03 1.044184e-02
## 32  2.419489e-01 1.531076e-01 5.208846e-01 6.301460e-02 8.234374e-03
## 33  5.023080e-01 2.808881e-01 1.767194e-01 2.031722e-02 3.670749e-03
## 34  5.821389e-01 3.452145e-01 5.246339e-02 1.350131e-03 3.139993e-04
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## 152 3.910765e-01 9.066302e-02 4.645221e-02 4.417787e-01 2.258657e-02
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## 155 7.968166e-01 7.454239e-02 6.319218e-03 1.489718e-02 7.716282e-02
## 
## $contrib
##            Dim.1        Dim.2        Dim.3        Dim.4        Dim.5
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## 68  2.600834e-02 0.1898897386 7.795554e-02 6.448934e-01 3.657648e-02
## 69  9.705995e-03 1.4325957944 3.039536e-01 1.770063e-02 1.835113e-01
## 70  5.753358e-02 0.0447202906 4.883140e-02 9.287818e-01 3.944592e-01
## 71  8.848180e-01 0.3463390428 3.570468e-02 2.149020e+00 1.455436e-01
## 72  4.158388e-03 0.0210084818 1.498562e-01 1.886959e+00 1.339131e-01
## 73  1.221917e-02 0.7626497108 6.003603e-01 4.285887e-01 1.509633e-01
## 74  5.215003e-03 0.1004617520 8.857022e-02 3.182506e-02 3.000577e-03
## 75  5.997402e-04 2.5306719393 4.219101e-02 4.063948e-01 3.051884e-01
## 76  1.896308e-02 0.0675774708 1.903126e-01 6.717026e-01 1.477513e-02
## 77  1.925577e-02 0.4160360220 4.624109e-01 1.177263e+00 8.376558e-01
## 78  5.851242e-02 0.0373830639 8.951103e-01 3.365785e-01 4.175147e-02
## 79  1.814675e-03 1.2326638684 2.187292e-01 5.553659e-01 1.829140e+00
## 80  4.688347e-01 0.1326659141 2.386924e-02 1.737015e+00 3.744469e-01
## 81  6.521808e-04 0.9529562370 6.094218e+00 6.852792e-02 3.903532e-03
## 82  1.455068e-02 2.2621428223 7.242579e-06 2.680355e-01 1.610583e+00
## 83  9.401221e-03 0.6984936306 1.435933e-01 1.004959e+00 2.675612e-01
## 84  1.570235e-01 0.4150988364 1.226432e+00 7.439268e-03 3.034206e+00
## 85  2.885491e-05 0.3228700200 1.965836e+00 1.913504e-02 3.781517e-04
## 86  6.578072e-02 0.0120054592 9.725855e-02 1.096932e+00 7.142008e-04
## 87  1.868132e-02 4.0508888056 1.242599e-02 1.641655e+00 5.110182e-02
## 88  1.037876e-02 0.3185562601 7.492414e-01 4.331087e-01 2.682162e-01
## 89  9.336804e-02 1.1541647461 8.576712e-04 7.082311e-01 8.544606e-01
## 90  9.675363e-02 0.2815815629 2.194412e+00 9.704819e-01 1.713964e-01
## 91  8.462201e-02 0.0346370118 1.011062e-01 3.725086e-05 1.007844e-02
## 92  7.981111e-03 0.1784412456 3.591874e-01 9.525668e-02 8.179752e-03
## 93  5.427741e-01 3.8820793166 2.699847e+00 5.579442e-01 9.796207e-02
## 94  6.089344e-03 0.0208561325 1.206020e-03 1.132024e+00 3.877601e-01
## 95  4.298466e-01 0.0017428476 3.240643e-02 1.747035e+00 9.330693e-01
## 96  2.829366e-02 0.1574740898 4.091010e-02 4.031404e-01 1.320826e-01
## 97  1.852020e-02 1.3305578165 5.554636e-01 3.619324e-02 4.720228e-01
## 98  1.735971e-02 0.2923539881 2.187316e+00 7.053797e-01 1.872090e-01
## 99  1.164089e-01 0.4686796408 8.984749e-01 2.613055e-01 8.178566e-04
## 100 4.785049e-04 0.0180474418 1.460568e+00 3.281820e-01 6.063619e-01
## 101 6.697322e-02 0.0580585300 2.491658e-01 2.344601e+00 9.535775e-01
## 102 2.079808e-01 0.8011644900 6.325197e-01 4.650107e-01 1.018065e+00
## 103 2.159299e-01 0.4938517111 8.025140e-02 8.515343e-02 1.275867e-01
## 104 1.738662e-01 0.2847714297 2.915457e-01 4.094241e-01 2.446764e-02
## 105 3.059400e-03 1.7018192276 1.706028e-01 1.661579e-02 1.445666e-01
## 106 1.140634e+00 0.3468402153 1.837401e-02 4.148436e-01 9.279631e-01
## 107 7.170909e-01 0.1499194786 1.637923e-02 9.899167e-01 4.577635e-03
## 108 2.119170e-01 0.0045105959 1.245507e+00 2.691238e+00 2.090048e+00
## 109 1.288701e-01 0.1510403360 2.650508e-02 2.965782e-01 3.787718e+00
## 110 3.177060e-01 0.0930143027 6.696841e-01 4.001080e-03 2.671043e+00
## 111 1.275027e-01 0.0548925189 1.178566e+00 1.305532e+00 1.700419e-01
## 112 3.123760e-01 1.0437441968 1.025866e+00 3.547056e-01 1.509147e-03
## 113 7.506315e-01 1.9312681489 3.843713e-01 4.188771e-01 5.631820e-02
## 114 5.239836e-02 8.3540263292 5.080087e+00 7.003198e-02 3.543360e-01
## 115 3.164565e-01 0.0040518725 1.422182e-01 2.420454e-01 3.220695e-01
## 116 4.365621e-01 0.1997268945 2.456196e-02 6.964387e-01 2.942747e-02
## 117 1.838027e-01 0.0962062933 3.530632e-02 6.090360e-01 4.228424e-02
## 118 2.106697e-01 0.8527704228 6.616879e-01 2.399865e-02 1.517889e-01
## 119 5.574124e-01 0.9477304823 1.001917e-01 7.483049e-03 2.622140e-02
## 120 7.923525e-03 0.8601046284 1.869341e+00 2.451616e-01 2.708132e-01
## 121 3.950410e-01 1.3290396195 1.723490e-03 4.779906e-01 1.621515e-01
## 122 3.562645e-01 0.1327427892 1.389528e-01 4.602025e-03 2.249393e+00
## 123 6.298704e-01 0.1238152348 2.726274e-03 6.581444e-01 5.354309e+00
## 124 5.792986e-01 0.0024604719 7.696138e-01 2.440256e-01 7.019843e-01
## 125 2.770081e-01 0.0225214283 4.012035e+00 3.349384e+00 2.083828e+00
## 126 1.202387e+00 0.0205927595 1.230870e-01 3.600091e-01 3.015464e+00
## 127 6.693920e-01 0.0009270660 1.099908e-01 4.869906e-01 2.955842e+00
## 128 8.415041e-01 0.4072041026 8.556257e-01 6.329149e-01 6.013197e-02
## 129 1.664186e-01 1.2511631522 2.103237e-01 1.543293e+00 1.325569e+00
## 130 9.707129e-01 0.4840394941 4.489449e-02 1.131077e+00 4.919698e+00
## 131 7.312963e-01 0.0724313475 3.441198e-02 3.568399e-01 5.643459e-01
## 132 2.598574e-01 0.6091523416 1.497339e+00 4.999132e-01 2.135220e+00
## 133 6.756257e-01 0.5005980931 1.757102e-01 1.235308e+00 2.394905e-01
## 134 9.148193e-01 0.2350342401 5.511077e-02 6.779354e-02 7.868838e-01
## 135 9.702786e-01 0.3711172702 2.592736e-01 1.406151e-01 3.219912e-01
## 136 1.364677e+00 0.9031420188 5.379040e-02 7.343706e-02 3.363684e+00
## 137 1.836215e+00 0.0006970406 1.987918e-03 7.438515e-03 1.667463e+00
## 138 9.813233e-01 0.0415306262 1.643455e-01 3.646069e-01 7.728122e-01
## 139 9.013791e-01 0.0887521394 7.454190e-01 1.372937e+00 2.316290e+00
## 140 1.572966e+00 0.8772300913 1.065411e-01 6.940718e-01 5.188882e+00
## 141 2.301966e+00 0.1003757490 1.780361e-01 1.662169e+00 5.762428e-04
## 142 1.213422e-01 0.0649125672 8.902040e-01 1.088811e+00 8.388292e-03
## 143 1.727067e+00 0.3532638885 4.058390e-01 2.873434e-02 3.400288e+00
## 144 1.383209e+00 0.0104737060 7.984871e-03 2.474564e-01 1.474960e-01
## 145 1.987552e+00 0.6986885973 1.674483e+00 4.756665e+00 3.308033e-01
## 146 1.172579e+00 0.2194475287 2.506160e-01 1.486631e-03 3.405935e-02
## 147 2.115326e+00 0.2440404828 1.373195e-02 1.566876e+00 3.954565e-02
## 148 1.671450e+00 0.2290417648 4.720864e-02 2.724789e-01 6.668235e-05
## 149 1.490594e+00 0.5503983261 1.117626e-01 1.481447e-02 1.572143e-04
## 150 1.909020e+00 0.3857020654 6.782110e-01 3.289567e-02 2.486470e+00
## 151 1.421440e-01 4.1249351728 7.021400e+00 1.426029e-01 5.074818e-01
## 152 1.265933e+00 0.8188061175 8.437213e-01 9.929882e+00 7.635682e-01
## 153 6.763604e-01 0.4148325501 3.693469e-01 9.205016e-02 1.012877e-02
## 154 3.094233e+00 0.0065212314 3.992960e-02 1.289661e+00 3.441885e-01
## 155 4.378049e+00 1.1426874900 1.948181e-01 5.683509e-01 4.427708e+00
## 
## $dist
##         1         2         3         4         5         6         7 
## 3.7961035 4.1478569 3.5948364 3.9423821 3.8819573 3.5599926 3.5802087 
##         8         9        10        11        12        13        14 
## 4.2771633 3.9812266 3.7712694 2.3531703 2.2571248 2.8323526 2.6441144 
##        15        16        17        18        19        20        21 
## 3.5471560 3.0391409 2.7804734 3.5867928 3.3164468 2.0117734 3.3224888 
##        22        23        24        25        26        27        28 
## 1.7004624 2.4848721 2.1433050 1.7055458 4.4875220 3.4535786 2.1334301 
##        29        30        31        32        33        34        35 
## 1.5096120 1.8913199 2.3286515 3.1644011 2.0317777 2.4076524 4.2138451 
##        36        37        38        39        40        41        42 
## 1.6802230 2.3449152 2.0540685 2.1244585 2.2945241 2.3055451 1.8151559 
##        43        44        45        46        47        48        49 
## 1.2256896 1.2886774 1.5230353 1.9055964 2.8612398 2.4617638 2.1967844 
##        50        51        52        53        54        55        56 
## 1.2966464 2.4465352 2.7794356 1.9108396 1.5677954 2.2211214 1.8100399 
##        57        58        59        60        61        62        63 
## 1.7483685 1.3670924 1.8632259 1.1586555 1.6201893 2.1256675 1.2989620 
##        64        65        66        67        68        69        70 
## 1.4624049 1.8066994 1.9702201 1.5199751 1.1072619 1.8706845 1.2703908 
##        71        72        73        74        75        76        77 
## 2.9515503 1.3751355 1.6665657 0.6357865 2.4232389 1.0990845 1.7294346 
##        78        79        80        81        82        83        84 
## 1.3197147 2.1233045 2.3743331 2.9523132 2.4476885 1.6389632 2.2106725 
##        85        86        87        88        89        90        91 
## 1.6697843 1.3345677 3.1908318 1.4565673 2.2125182 2.0946386 0.9519184 
##        92        93        94        95        96        97        98 
## 0.9949776 3.9650746 1.3902687 2.3873386 1.2983809 2.0150897 2.0441449 
##        99       100       101       102       103       104       105 
## 1.7889742 1.6271956 2.0611503 2.1284793 1.6995077 1.5172962 2.1378658 
##       106       107       108       109       110       111       112 
## 3.0234079 2.4102446 2.5106647 1.9217531 2.1105581 1.9227011 2.3266386 
##       113       114       115       116       117       118       119 
## 3.0731606 4.8556037 1.6640172 1.9224553 1.4893784 2.1606797 2.3676957 
##       120       121       122       123       124       125       126 
## 2.3705754 2.4218230 1.9796189 2.7404955 2.2592936 3.1713533 3.1210155 
##       127       128       129       130       131       132       133 
## 2.4717214 2.7883193 2.5350895 3.2326160 2.3032299 2.5862987 2.5375579 
##       134       135       136       137       138       139       140 
## 2.5395970 2.7393995 3.4577211 3.4660498 2.6053223 2.9972400 3.9476201 
##       141       142       143       144       145       146       147 
## 3.9470395 2.1525118 3.6524912 2.9700961 4.3732562 2.7762392 3.7942431 
##       148       149       150       151       152       153       154 
## 3.2788041 3.1686328 3.7559724 4.3044701 4.3514342 2.4381924 4.4481515 
##       155 
## 5.6691645
#Plotting the graph
fviz_pca_ind (happy.pca, pointsize = "cos2", pointshape = 22, fill = "blue", repel = TRUE)

#Method to show only the 50 countries best represented.
plot(happy.pca,  select = "cos2 50", cex=1,  col.ind = "darkblue", title = "50 countries with highest cos2", cex.main=2, col.main= "darkblue")